Low-voltage electric leakage telemetering system based on Bluetooth communication and related device
Through the low-voltage leakage telemetry system based on Bluetooth communication, the problems of low efficiency and safety risks of traditional low-voltage leakage detection methods are solved, and more efficient and accurate leakage fault location and repair are achieved.
Patent Information
- Application Number
- CN202411584914.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-07
- Publication Date
- 2025-03-25
AI Technical Summary
Traditional low-voltage leakage detection methods are inefficient, making it difficult to quickly determine the location of specific fault points, and there are safety risks when inspecting complex terrain.
A low-voltage leakage telemetry system based on Bluetooth communication is adopted. The system includes a leakage detection module, a Bluetooth transmission module, a data analysis module, a fault positioning module, a fault repair module and an acceptance module. The leakage detection data is transmitted through Bluetooth to perform data analysis and fault positioning, and a fault repair plan is generated and accepted.
It improves the accuracy and efficiency of the low-voltage leakage detection process, can locate and repair leakage faults faster and safer, and reduces labor cost investment.
Smart Images
Figure CN119689321A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing and low-voltage leakage detection, and in particular to a low-voltage leakage remote sensing system based on Bluetooth communication and related devices. Background Art
[0002] With the improvement of living standards, the number of low-voltage users' electrical equipment has also increased. Due to the uneven quality of electrical equipment used by low-voltage users, leakage is prone to occur when a fault occurs or when the line is aging or the insulation layer is damaged during daily operation. This will not only cause power loss, but also pose a risk of electric shock. Traditional leakage detection methods mainly use leakage clamp meters to perform "line elimination method" and "intuitive inspection method". The above methods are inefficient and cannot quickly determine the specific fault point location. In addition, when encountering complex terrain, it is necessary to climb ladders to perform the inspection, which poses certain safety risks. Therefore, how to improve the accuracy of the low-voltage leakage detection process has become a problem that needs to be solved urgently. Summary of the invention
[0003] The present invention provides a low-voltage leakage remote sensing system and related devices based on Bluetooth communication to solve the technical problem of how to improve the accuracy of the low-voltage leakage detection process.
[0004] In a first aspect, a low-voltage leakage telemetry system based on Bluetooth communication is provided, wherein the low-voltage leakage telemetry system based on Bluetooth communication includes a leakage detection module, a Bluetooth transmission module, a data analysis module, a fault location module, a fault repair module and an acceptance module, wherein:
[0005] The leakage detection module performs leakage detection on the target low-voltage household line to obtain leakage detection data;
[0006] A Bluetooth transmission module transmits leakage detection data to a data analysis module via Bluetooth;
[0007] The data analysis module analyzes the received leakage detection data to obtain reference fault analysis data;
[0008] A fault location module performs fault location according to reference fault analysis data and leakage detection data to obtain fault location data;
[0009] The fault repair module generates a fault repair plan based on the fault location data, reference fault analysis data and leakage detection data;
[0010] The fault acceptance module obtains the fault acceptance data after the fault is repaired according to the fault repair plan, performs acceptance analysis on the fault acceptance data, and generates a fault acceptance result.
[0011] In a second aspect, a low-voltage leakage remote sensing device based on Bluetooth communication is provided, wherein:
[0012] The first processing module is used to perform leakage detection on the target low-voltage household incoming line to obtain leakage detection data;
[0013] A transmission module, used to transmit leakage detection data to a data analysis module via Bluetooth;
[0014] The second processing module is used to analyze the received leakage detection data to obtain reference fault analysis data;
[0015] A third processing module is used to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data;
[0016] A fourth processing module, used to generate a fault repair plan based on the fault location data, reference fault analysis data and leakage detection data;
[0017] The fifth processing module is used to obtain fault acceptance data after the fault is repaired according to the fault repair plan, and perform acceptance analysis on the fault acceptance data to generate a fault acceptance result.
[0018] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processing steps of the low-voltage leakage telemetry system based on Bluetooth communication are implemented.
[0019] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processing steps of the low-voltage leakage telemetry system based on Bluetooth communication are implemented.
[0020] In the scheme implemented by the above-mentioned low-voltage leakage telemetry system and related devices based on Bluetooth communication, the leakage detection module performs leakage detection on the target low-voltage household line to obtain leakage detection data, and transmits the leakage detection data to the data analysis module via Bluetooth through the Bluetooth transmission module, so that the data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data, so that the fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, so as to further enable the fault repair module to generate a fault repair plan according to the fault location data, reference fault analysis data and leakage detection data, and then enable the fault acceptance module to obtain the fault acceptance data after the fault is repaired according to the fault repair plan, and perform acceptance analysis on the fault acceptance data to generate a fault acceptance result, which is conducive to improving the accuracy of the low-voltage leakage detection process and can achieve the purpose of more efficient and accurate low-voltage leakage detection. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.
[0022] Figure 1 It is a schematic diagram of an application environment of a low-voltage leakage remote sensing system based on Bluetooth communication in one embodiment of the present invention;
[0023] Figure 2 It is a processing flow diagram of a low-voltage leakage remote sensing system based on Bluetooth communication in one embodiment of the present invention;
[0024] Figure 3 It is a structural schematic diagram of a low-voltage leakage remote sensing device based on Bluetooth communication in one embodiment of the present invention;
[0025] Figure 4 is a schematic diagram of a structure of a computer device in one embodiment of the present invention;
[0026] Figure 5 It is another structural schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION
[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0028] With the improvement of living standards, the number of low-voltage user electrical equipment continues to increase. Due to the uneven quality of electrical equipment and the aging of lines and the damage of insulation layers, leakage occurs frequently, which not only causes power loss, but also brings safety risks of electric shock. Traditional leakage detection methods are inefficient, difficult to determine the specific fault point, and there are safety risks when troubleshooting in complex terrain. Without solving the above problems, the low-voltage leakage telemetry system based on Bluetooth communication provided by the embodiment of the present invention can be applied to Figure 1 In the application environment, the client communicates with the low-voltage leakage telemetry system based on Bluetooth communication through the network. Figure 1 As shown, the low-voltage leakage telemetry system based on Bluetooth communication may include a leakage detection module, a Bluetooth transmission module, a data analysis module, a fault location module, a fault repair module and an acceptance module.
[0029] Exemplarily, taking the scenario of power grid fault early warning as an example, the management personnel can receive analysis data and fault acceptance results from the low-voltage leakage telemetry system based on Bluetooth communication through the client. The leakage detection module in the low-voltage leakage telemetry system based on Bluetooth communication performs leakage detection on the target low-voltage household line to obtain leakage detection data, and transmits the leakage detection data to the data analysis module through Bluetooth through the Bluetooth transmission module, so that the data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data, so that the fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, so that the fault repair module generates a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data, and then the fault acceptance module obtains the fault acceptance data after the fault is repaired according to the fault repair plan, and performs acceptance analysis on the fault acceptance data to generate a fault acceptance result, and feeds the fault acceptance result back to the client. Correspondingly, the client can receive the fault acceptance result from the server, and can display the fault acceptance result on the client for the target user to query or browse. By adopting the low-voltage leakage telemetry system based on Bluetooth communication provided by this application, it is beneficial to improve the accuracy of the low-voltage leakage detection process, and the purpose of more efficient and accurate low-voltage leakage detection can be achieved.
[0030] The client may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server may be implemented by an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.
[0031] See also Figure 2 As shown, Figure 2 A schematic diagram of a processing flow of a low-voltage leakage telemetry system based on Bluetooth communication provided by an embodiment of the present invention includes the following steps:
[0032] S10: The leakage detection module performs leakage detection on the target low-voltage service line to obtain leakage detection data.
[0033] Among them, the leakage detection module is mainly responsible for detecting the leakage of the low-voltage line. The leakage detection module can include a high-precision current sensor. For example, the leakage detection module can use a high-precision current sensor to perform leakage detection on the target low-voltage service line to obtain leakage detection data. The high-precision current sensor is based on the principle of electromagnetic induction and can sensitively detect the weak leakage current in the target low-voltage service line. When current passes through the wire, a magnetic field will be generated around it. The high-precision current sensor can determine the magnitude and direction of the current by detecting changes in this magnetic field.
[0034] Optionally, the leakage detection module can utilize the leakage detection data acquisition function of the current mature leakage detection clamp meter to realize leakage current detection of the overhead low-voltage service line. Optionally, an infrared communication module can be added to the leakage detection clamp meter, and the leakage detection clamp meter with the communication module can be connected through a handheld device, so that the leakage data detected by the leakage detection clamp meter can be read. Optionally, a threaded detachable insulating ring rod can be added to the bottom of the leakage detection clamp meter to realize long-distance telemetry and long-distance reading of leakage detection data.
[0035] The design idea of the wireless leakage detection device module (i.e., the leakage detection module) involved in this application can be as follows: According to Kirchhoff's current law, the current vector and non-zero current flowing through the phase line and neutral line loop at a certain cross-sectional position of the low-voltage household line are measured, which is the leakage current. The leakage type clamp current sensor adopts a portable clamp design, and the jaws are a push-pull opening and closing structure. Push the jaws forward to open and clamp the measured wire, and pull the jaws back to open and leave the measured wire.
[0036] The leakage data sampling operation unit can be composed of a sampling circuit and a metering chip. During sampling, the current signal collected by the clamp current sensor can be converted into a voltage signal through a sampling resistor and input into the metering chip. At the same time, the calibration parameters are called to obtain the effective value of the detection current.
[0037] The leakage clamp current sensor has the characteristics of wide current measurement range and high current accuracy. The specific parameters are as follows: the current measurement range can be 0-1200A, the accuracy can be level 1, and the resolution can be 0.01mA. By using the mature leakage clamp meter detection function, the current vector and non-zero current value calculated by the clamp current transformer at a certain cross-section position of the low-voltage service line can be measured to complete the leakage current measurement, thereby achieving the purpose of measuring the leakage current of the low-voltage service line.
[0038] Optionally, the leakage detection module involved in the present application may include a wireless data acquisition receiving module, and the design idea of the wireless data acquisition receiving module may be as follows: by adding a wireless communication module to the leakage detection module, the leakage data detected is wirelessly transmitted. Among them, the wireless transmission distance may be about 30m. Then, by connecting the wireless data receiver to the leakage detection device with the communication module, the leakage data can be viewed and read from a distance.
[0039] Optionally, the leakage detection module involved in the present application may include an insulating handheld connecting rod (i.e., an insulating link rod), the design concept of which is as follows: by designing a threaded connection component at the bottom of the leakage detection clamp meter, it can be fixedly connected to the insulating link rod, and the handheld insulating link rod can be used to remotely measure the leakage value of the low-voltage service line, and there is no need to climb high, thereby reducing the safety risk of the operation. Among them, the insulating link rod can be made of high-strength, corrosion-resistant insulating material, and the length can be adjusted according to actual needs to adapt to different terrains and working heights, and this application does not limit this.
[0040] S20: The Bluetooth transmission module transmits the leakage detection data to the data analysis module via Bluetooth.
[0041] Among them, the Bluetooth transmission module can be used to transmit leakage detection data from the leakage detection module to other modules (such as the data analysis module). When the leakage detection module obtains the leakage detection data, the Bluetooth transmission module is immediately started to encode the leakage detection data in the form of a digital signal and send the encoded signal through Bluetooth wireless communication technology. Bluetooth transmission has the characteristics of short distance, low power consumption and high speed, and can quickly and stably transmit data to the data analysis module.
[0042] Optionally, the Bluetooth transmission module has automatic retransmission and error checking functions. When signal interference or data loss occurs during transmission, the data can be automatically resent to ensure data integrity. Optionally, in order to ensure the security of data transmission, the Bluetooth transmission module can use encryption technology to prevent data from being illegally stolen or tampered with.
[0043] The data analysis module may be a data analysis module in a handheld device of a staff member, or may be a data analysis module in a cloud data management and analysis platform, and this application does not limit this. Optionally, this application uses the data analysis module as a data analysis module in a cloud data management and analysis platform as an example for illustration, which does not constitute a limitation on this application.
[0044] The leakage detection module can establish a connection with other devices, such as the staff's handheld devices (such as smartphones and tablets) through the Bluetooth transmission module. A dedicated application can be installed on the handheld device to receive, display and analyze leakage detection data. The staff can view the leakage data, historical records and fault analysis results in real time on the handheld device.
[0045] Furthermore, the handheld device can synchronize the received leakage detection data to the data management and analysis platform (such as the above-mentioned data analysis module) for centralized storage and analysis. It is understandable that the data management and analysis platform can use big data analysis technology and artificial intelligence algorithms in subsequent steps to conduct in-depth mining of leakage detection data to quickly and accurately determine the location of the fault point and generate a detailed fault report. Multiple staff members can access the data management and analysis platform anytime and anywhere through multiple terminal devices to obtain the latest analysis data, and this application does not limit this.
[0046] S30: The data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data.
[0047] The data analysis module can conduct in-depth analysis on the received leakage data to provide a basis for fault location and repair in the subsequent steps. The data analysis module can receive leakage detection data uploaded synchronously from handheld devices, and use big data analysis technology and artificial intelligence algorithms to conduct in-depth mining of leakage data to analyze the characteristics and change trends of leakage data, and then determine whether there is a leakage fault and the severity of the fault.
[0048] The data analysis module may include a data receiving submodule, a data preprocessing submodule, a fault feature extraction submodule, a fault pattern matching submodule, a fault trend analysis submodule, and an analysis data generation submodule. It should be understood that the data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data, which refers to the process of how to obtain reference fault analysis data. Among them, in step S30, that is, the data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data, which may include the following steps:
[0049] S31: the data receiving submodule receives the leakage detection data and arranges the leakage detection data in time sequence to obtain time sequence detection data;
[0050] S32: the data preprocessing submodule preprocesses the time series detection data to obtain preprocessed detection data;
[0051] S33: the fault feature extraction submodule extracts fault features from the pre-processed detection data to obtain leakage fault feature data;
[0052] S34: the fault mode matching submodule matches the leakage fault characteristic data with the leakage mode data in the leakage mode database to obtain a target leakage fault mode;
[0053] S35: the fault trend analysis submodule performs trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result;
[0054] S36: The analysis data generation submodule generates reference fault analysis data according to the fault trend analysis result.
[0055] After receiving the leakage detection data from the Bluetooth transmission module, the data receiving submodule can perform preliminary sorting of the leakage detection data, such as performing preliminary format check and error checking on the data to ensure the integrity and accuracy of the data. For example, if the data receiving submodule finds that the data is erroneous or incomplete, it can request the Bluetooth transmission module to resend the data. The data receiving submodule can sort the data in chronological order to establish a data time series, i.e., the above-mentioned timing detection data, thereby providing a basis for subsequent analysis.
[0056] First, the data preprocessing submodule can remove noise from the timing detection data, such as using digital filtering technology, such as median filtering, mean filtering, etc., to remove high-frequency noise and random interference in the data. Optionally, the characteristics and sources of the noise can be further analyzed to adjust the filtering parameters according to the actual situation to achieve the best denoising effect. Secondly, the data preprocessing submodule can perform data calibration on the denoised data, such as comparing with known standard values or reference values to calibrate the leakage detection results. Optionally, the impact of environmental factors on the measurement results, such as temperature, humidity, etc., can be further considered to perform corresponding compensation calibration on the denoised data.
[0057] The data preprocessing submodule can detect outliers in the data, such as data points that obviously deviate from the normal range, to achieve outlier processing of the data after data calibration, to preliminarily screen outliers caused by measurement errors or interference, and to focus on real fault signals, thereby obtaining preprocessed detection data. Optionally, statistical methods such as box plot method, Laida criterion, etc. can be used to identify outliers.
[0058] The feature extraction submodule can extract features from the pre-processed detection data. Optionally, the feature extraction submodule can calculate statistical features, such as the mean, variance, standard deviation, median and other statistical parameters of the pre-processed detection data, to reflect the central tendency and dispersion of the data. By analyzing the changes in statistical features over time, the stability and change trend of leakage can be determined.
[0059] Optionally, the feature extraction submodule can extract time domain features, such as calculating the peak value, valley value, peak-to-peak value and other time domain features of the current, to understand the amplitude variation range of the leakage current. By analyzing the current rise time, fall time, duration and other features, the occurrence time and duration of the leakage can be determined.
[0060] Optionally, the feature extraction submodule can convert the time domain data into frequency domain data by extracting frequency domain features, such as performing Fourier transform on the leakage detection results. By analyzing the spectrum distribution of the frequency domain data and extracting frequency domain features such as the main frequency, harmonic content, and frequency bandwidth, the frequency component of the leakage signal can be understood.
[0061] The leakage mode database may include typical features and modes of various leakage faults, such as ground leakage, phase leakage, insulation damage leakage, etc. It is understandable that different types of leakage faults may have different features, and the leakage mode database may be established and obtained by analyzing and summarizing a large amount of actual fault data.
[0062] The fault pattern matching submodule can match the extracted leakage fault feature data with each leakage pattern data in the leakage pattern database to determine which leakage fault type the leakage detection data belongs to. Optionally, a pattern recognition algorithm, such as a support vector machine, a neural network, etc., can be used for classification and identification, which is not limited in this application.
[0063] The fault trend analysis submodule can perform trend analysis and prediction on the leakage detection data according to the target leakage fault mode. The time series analysis method can be used to analyze the changing trend of the leakage current and determine whether the fault is gradually getting worse or better, thereby providing a decision-making basis for fault repair. The correlation analysis submodule can be used to analyze the correlation between the leakage detection results and other related factors, such as voltage changes, load changes, environmental factors, etc., to find out the factors that may affect the leakage, thereby providing a reference for fault diagnosis and prevention.
[0064] Optionally, the fault trend analysis submodule includes a relevant data acquisition submodule, a fluctuation scale analysis submodule, a weighted parameter determination submodule, a leakage fault prediction submodule and a result evaluation submodule. It should be understood that the fault trend analysis submodule performs trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result, which refers to the process of how to obtain the fault trend analysis result. Among them, in step S35, that is, the fault trend analysis submodule performs trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result, which may include the following steps:
[0065] S351: The relevant data acquisition submodule acquires fault characteristic data related to the target leakage fault mode to obtain relevant fault characteristic data;
[0066] S352: the fluctuation scale analysis submodule performs fluctuation scale analysis according to the relevant fault characteristic data and the leakage fault characteristic data to obtain fluctuation scale data;
[0067] S353: the weighted parameter determination submodule determines a weighted movement parameter according to the fluctuation scale data;
[0068] S354: the leakage fault prediction submodule predicts the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set;
[0069] S355: The result evaluation submodule performs result evaluation based on the predicted leakage fault data set to obtain a fault trend analysis result.
[0070] The relevant data acquisition submodule can be used to collect fault characteristic data related to the target leakage fault mode. The relevant fault characteristic data may include but is not limited to leakage current value, voltage change, ground resistance value, equipment operation time, ambient temperature and humidity, etc. The specific data type may depend on the system's monitoring capability and requirements for leakage faults, and this application does not limit this.
[0071] The relevant data acquisition submodule can determine the characteristic signs of the target leakage fault mode. For example, for the ground leakage fault mode, it can obtain the change of ground resistance, leakage current of a specific phase, etc., to further filter out the fault characteristic data related to these characteristic signs from the database, and preliminarily organize and verify the filtered data to ensure the accuracy and completeness of the data, so as to obtain the above-mentioned relevant fault characteristic data.
[0072] The fluctuation scale analysis submodule can analyze the fluctuation scale of the data based on the existing relevant fault feature data and the current detected leakage fault feature data. The fluctuation scale can reflect the change amplitude and frequency of the data at different time points. The fluctuation scale can be used to determine the parameters of the weighted moving average in the subsequent steps.
[0073] The fluctuation scale analysis submodule can merge and align the relevant fault feature data with the leakage fault feature data to ensure the consistency of the data in the time series, and calculate the change of the data at different time points, such as the absolute value and variance of the difference between the data at adjacent time points. Statistics such as the fluctuation scale can be used to measure the fluctuation scale. It is understandable that the fluctuation scale calculation can be performed separately for different fault feature data to obtain the fluctuation scale data corresponding to each fault feature, and this application does not limit this.
[0074] The weighted parameter determination submodule can determine the parameters of the weighted moving average according to the fluctuation scale data, that is, the weighted moving parameters. The weighted moving parameters may include but are not limited to the time window length and the weight coefficient. The weighted parameter determination submodule can determine the change trend and stability of the data by analyzing the fluctuation scale data. If the data fluctuates greatly, a shorter time window and a larger recent data weight can be used; if the data is relatively stable, a longer time window and a relatively balanced weight coefficient can be selected.
[0075] Optionally, the weighted parameter determination submodule can use some empirical formulas or algorithms to determine the weighted movement parameters. For example, the time window length can be set to a certain multiple of the fluctuation period according to the size of the fluctuation scale; for the weight coefficient, the exponential decrease method can be used to adjust the attenuation factor according to the fluctuation scale, and this application does not limit this. After calculation and adjustment, the weighted parameter determination submodule can determine the final weighted movement parameters, which can include the time window length and the weight coefficients of each time point.
[0076] The leakage fault prediction submodule can use the weighted moving parameters and fault characteristic data to predict the leakage fault data of the preset time period to obtain a predicted leakage fault data set. The weighted parameter determination submodule can perform weighted moving average calculation on the fault characteristic data according to the time window length and weight coefficient determined by the weighted moving parameters.
[0077] Optionally, the leakage fault prediction submodule uses the following formula to predict the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set:
[0078]
[0079] Among them, LD trepresents the predicted leakage fault data at time point t in the predicted leakage fault data set; m represents the time window length; i represents the index from 1 to the time window length m in the weighted moving average calculation; w i Indicates the weight coefficient corresponding to time point t-i+1 when the time window length is m; y t-i+1 Represents the actual data value at time point t-i+1 in the time series. It can be understood that as i changes, t-i+1 can represent different past time points relative to time point t.
[0080] When calculating the weighted moving average, for each time point t, the data y at different time points are t-i+1 And the corresponding weight coefficient w i Multiply and then add up to get the weighted moving average LD at time point t t , which is the predicted leakage fault data point. It can be understood that different weight coefficient settings will lead to different weighted moving average results, thus affecting the analysis and prediction of data trends.
[0081] Starting from the current time point, the time window is gradually moved forward, and the data at each time point is weighted averaged to obtain the predicted leakage fault data point. Further, these predicted data points are connected in time sequence to form a leakage fault time series prediction curve.
[0082] The result evaluation submodule can evaluate the result according to the predicted leakage fault data set (or according to the leakage fault timing prediction curve mentioned above) to obtain the fault trend analysis result. The weighted parameter determination submodule can analyze the trend of the leakage fault timing prediction curve to determine the development trend of the leakage fault. For example, if the curve shows an upward trend, it may indicate that the leakage fault may be getting worse; if the curve drops, it may indicate that the fault may be alleviated; if the curve is relatively stable, it may indicate that the fault may be in a stable state.
[0083] Optionally, some evaluation indicators, such as the slope of the curve, prediction error, etc., can be used to quantitatively evaluate the accuracy and reliability of the prediction results. The result evaluation submodule can generate a fault trend analysis report based on the evaluation results, that is, the above-mentioned fault trend analysis results. The fault trend analysis results may include predictions of future leakage fault development, possible influencing factors, and recommended countermeasures.
[0084] The analysis data generation submodule can generate detailed reference fault analysis data based on the above fault trend analysis results. The reference fault analysis data may include but is not limited to information such as the type of leakage, severity level, possible fault cause, development trend prediction, etc. Further, the reference fault analysis data can be sent to the fault location module to provide a basis for fault location. Optionally, the data can be stored for reference in subsequent fault analysis and processing.
[0085] S40: The fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data.
[0086] The fault location module can be used to accurately determine the specific location of the leakage fault. Optionally, the fault location module can combine the reference fault analysis data and the leakage detection results, use advanced algorithms and technologies, quickly and accurately determine the fault point location, and provide detailed fault location information to the staff for targeted troubleshooting and repair.
[0087] The fault location module can locate the fault based on the signal strength. For example, if the leakage signal strength detected at different locations is different, the approximate location of the fault point can be inferred by comparing the difference in signal strength. Optionally, the fault location module can combine the topological structure and electrical parameters of the low-voltage service line and use a mathematical model to accurately locate the fault. This application uses this as an example for illustration and does not constitute a limitation on this application.
[0088] The fault location module includes an electrical transformation calculation submodule, a phase relationship analysis submodule, a spectrum analysis submodule, and a propagation path analysis submodule. It should be understood that the fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, which refers to the process of how to obtain fault location data. Among them, in step S40, that is, the fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, which may include the following steps:
[0089] S41: the electrical conversion calculation submodule performs voltage conversion calculation and current conversion calculation according to the reference fault analysis data and the leakage detection data to obtain voltage basic conversion data and current basic conversion data;
[0090] S42: the phase relationship analysis submodule performs phase relationship analysis according to the voltage basic transformation data and the current basic transformation data to obtain phase relationship data;
[0091] S43: the spectrum analysis submodule performs spectrum analysis according to the phase relationship data, the voltage basic transformation data and the current basic transformation data to obtain harmonic content data;
[0092] S44: The propagation path analysis submodule combines the harmonic content data with the circuit model to analyze the harmonic propagation path to obtain fault location data.
[0093] The electrical transformation calculation submodule can be used to calculate basic electrical parameters, such as calculating the mean, variance, peak and other statistical parameters of the leakage current at different locations to understand the magnitude and variation range of the leakage current. The electrical transformation calculation submodule can analyze the variation of the voltage value, including the mean, fluctuation range, etc. Optionally, the electrical transformation calculation submodule can calculate the effective value, peak value and other parameters of the voltage.
[0094] The phase relationship analysis submodule can be used to analyze the phase relationship between current and voltage. For example, the current and voltage signals can be measured synchronously to determine the phase difference between them. It is understandable that under normal circumstances, the current and voltage should maintain a certain phase relationship, but in the case of a leakage fault, the phase relationship may change. By analyzing the trend of the phase difference, it can be determined whether there is a phase abnormality; for example, if the phase difference between current and voltage suddenly increases or decreases, it may mean that there is a leakage fault.
[0095] The spectrum analysis submodule can be used to analyze the harmonic content. Optionally, the leakage current and voltage signals can be Fourier transformed to analyze their spectrum distribution, thereby calculating the harmonic content of different frequency components, especially the content of higher harmonics. A leakage fault may cause an increase in harmonic content, especially within a specific frequency range. By analyzing the change in harmonic content, it can be determined whether there is a leakage fault and the possible location of the fault.
[0096] The circuit model can be established based on the actual circuit structure and connection mode of the low-voltage service line, such as a simple equivalent circuit model, such as a circuit composed of resistors, inductors, and capacitors, or a more complex distributed parameter model, which is not limited in this application. Factors such as power supply, load, and line impedance are considered in the circuit model to reflect the characteristics of the actual circuit as accurately as possible.
[0097] Optionally, according to the circuit model, the current and voltage values of each node, as well as parameters such as power loss in the line can be calculated. For example, circuit theories such as Ohm's law and Kirchhoff's law can be used to analyze the distribution of leakage current and voltage at different locations, so as to determine possible fault causes, such as ground faults, phase-to-phase short circuits, etc., and analyze the change rules of electrical parameters under different fault conditions. For example, in the case of a ground fault, the size of the grounding resistance will affect the distribution of the leakage current; in the case of a phase-to-phase short circuit, the size and direction of the short-circuit current will change significantly.
[0098] Furthermore, the topological structure can be combined for analysis, such as analyzing the topological structure of the low-voltage service line and determining the connection relationship between each node and line segment. Based on the topological structure, the propagation path of current and voltage, as well as the possible fault propagation path, can be determined. By considering the branching and parallel connection of the line, the sensitivity of electrical parameters at different locations to faults can be analyzed. For example, at the branch node, the current may be shunted, resulting in different leakage currents on different branches; in parallel lines, faults may propagate between different lines, affecting electrical parameters at multiple locations.
[0099] The harmonic content data can be input into the above circuit model, and the harmonic propagation path in the circuit can be simulated and analyzed to determine the contribution of harmonic sources at different locations to the harmonic content at the measurement point. For example, combined with the possible fault location hypothesis, the impact of a leakage fault on the harmonic propagation path and the change of harmonic content at each measurement point can be analyzed if a leakage fault occurs at the possible fault location, thereby obtaining fault location data.
[0100] Further judgment can be made in combination with the fault type (such as reference fault analysis data), such as further determining the location of the fault point based on the change of electrical parameters obtained by analyzing the leakage detection data and the possible fault type. For example, if the leakage current shows an increase in high-order harmonic content and a change in the voltage phase, it may be a leakage fault caused by a nonlinear load. At this time, the location of the nonlinear load can be checked in particular.
[0101] It is understandable that different judgment methods can be used for different types of faults. For example, for a ground fault, the location of the fault point can be determined by measuring the magnitude of the ground resistance; for a phase-to-phase short circuit fault, the fault location can be determined by comparing the magnitude and direction of the current between different phases, and this application does not limit this.
[0102] The goal of the fault location module is to determine the fault location as accurately as possible to improve the efficiency of fault repair. By continuously optimizing the positioning algorithm and technology, the positioning accuracy can be improved and the time and cost of troubleshooting can be reduced.
[0103] S50: The fault repair module generates a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data.
[0104] The fault repair module can be used to generate an optimized fault repair plan to ensure that the leakage fault can be solved in a timely and effective manner. The fault repair module can generate the optimal fault repair plan based on the fault location data, reference fault analysis data and leakage detection results, taking into account various factors. Among them, the fault repair plan may include but is not limited to specific repair steps, required maintenance materials and tools, estimated repair time, etc., and this application does not limit this. For example, if it is a ground leakage fault, it may be necessary to check the grounding line, replace the damaged grounding device, etc.
[0105] It is understandable that the fault repair scheme may have certain adaptability and flexibility, and the fault repair scheme may indicate different types of repair schemes according to different fault conditions. For example, for a serious leakage fault, emergency measures may need to be taken immediately for repair; while for a minor leakage fault, planned repairs may be carried out at an appropriate time, and this application does not limit this.
[0106] S60: The fault acceptance module obtains fault acceptance data after the fault is repaired according to the fault repair solution, and performs acceptance analysis on the fault acceptance data to generate a fault acceptance result.
[0107] The fault acceptance module can be used to test and confirm the fault repair effect. After the fault is repaired according to the fault repair plan, the fault acceptance module can start working, such as obtaining the fault acceptance data after the repair. The fault acceptance data may include but is not limited to the leakage detection data after the repair, the change of electrical parameters after the repair, etc., and this application does not limit this. The fault acceptance data can be obtained by re-detecting the leakage detection module, or it can be obtained through other monitoring equipment, and this application does not limit this.
[0108] The fault acceptance module can determine whether the leakage problem has been completely solved by comparing the leakage detection data and other relevant indicators before and after the repair. If the leakage current after the repair is within the allowable range and the electrical parameters return to normal, the fault can be considered to have been repaired successfully. At this time, a fault acceptance result that has passed the acceptance can be generated, and the fault repair situation can be fed back to the relevant personnel. If the leakage current after the repair is still not within the allowable range, or the electrical parameters are still abnormal, the fault repair can be considered to have failed. At this time, a fault acceptance result that has failed the acceptance can be generated, and further repair suggestions and guidance can be provided for secondary repair. This application does not limit this.
[0109] The low-voltage leakage telemetry system based on Bluetooth communication provided by this application can improve the efficiency of leakage detection. The traditional leakage detection method mainly uses ordinary leakage clamp meters to eliminate household by household or segmented detection, which is not only labor-intensive, but also cannot quickly and effectively determine the leakage location, and the work efficiency is low. The use of a low-voltage household line leakage telemetry device can complete the investigation of multiple households or one area, which not only reduces the labor intensity and greatly reduces the scope of investigation, but also can efficiently determine the leakage fault point, improving the efficiency of leakage detection.
[0110] The low-voltage leakage telemetry system based on Bluetooth communication provided by this application can improve the level of safe operation. When conducting on-site low-voltage leakage inspection, you may encounter problems such as the meter being installed at a high position, the user not being at home, or the wall being fenced off. You need to climb a ladder to work at height, which may pose a certain risk of falling. The low-voltage household line leakage telemetry device only requires you to hold an insulated operating rod and stand on the ground to complete the leakage inspection, which improves the safety level of leakage inspection.
[0111] The low-voltage leakage telemetry system based on Bluetooth communication provided by this application can reduce the cost of leakage investigation. The equipment used for traditional leakage investigation is generally mainly leakage clamp meters. When there are many users connected to the station area, the leakage investigation needs to arrange multiple people to conduct the investigation, which requires a lot of investment in manpower costs. By adopting a low-voltage household line leakage telemetry device, only two people are needed to complete the leakage investigation work, which greatly reduces the investment in manpower costs.
[0112] It can be seen that in the above scheme, the leakage detection module performs leakage detection on the target low-voltage service line to obtain leakage detection data, and transmits the leakage detection data to the data analysis module via Bluetooth through the Bluetooth transmission module, so that the data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data, so that the fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, so as to further enable the fault repair module to generate a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data, and then enable the fault acceptance module to obtain the fault acceptance data after the fault is repaired according to the fault repair plan, and perform acceptance analysis on the fault acceptance data to generate a fault acceptance result, which is conducive to improving the accuracy of the low-voltage leakage detection process and can achieve the purpose of more efficient and accurate low-voltage leakage detection.
[0113] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.
[0114] In one embodiment, a low-voltage leakage remote sensing device based on Bluetooth communication is provided, and the low-voltage leakage remote sensing device based on Bluetooth communication corresponds one-to-one with the low-voltage leakage remote sensing system based on Bluetooth communication in the above embodiment. Figure 3 As shown, the low-voltage leakage remote sensing device based on Bluetooth communication includes a first processing module 101, a transmission module 102, a second processing module 103, a third processing module 104, a fourth processing module 105 and a fifth processing module 106. The functional modules are described in detail as follows:
[0115] The first processing module 101 is used to perform leakage detection on the target low-voltage service line to obtain leakage detection data;
[0116] A transmission module 102, used for transmitting the leakage detection data to the data analysis module via Bluetooth;
[0117] The second processing module 103 is used to analyze the received leakage detection data to obtain reference fault analysis data;
[0118] A third processing module 104 is used to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data;
[0119] A fourth processing module 105, configured to generate a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data;
[0120] The fifth processing module 106 is used to obtain fault acceptance data after the fault is repaired according to the fault repair solution, and perform acceptance analysis on the fault acceptance data to generate a fault acceptance result.
[0121] In one embodiment, the second processing module 103 is used to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, specifically for:
[0122] Receiving the leakage detection data and arranging the leakage detection data in time sequence to obtain time sequence detection data;
[0123] Preprocessing the time series detection data to obtain preprocessed detection data;
[0124] Extracting fault features from the pre-processed detection data to obtain leakage fault feature data;
[0125] Matching the leakage fault characteristic data with the leakage mode data in the leakage mode database to obtain a target leakage fault mode;
[0126] Performing trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result;
[0127] According to the fault trend analysis result, reference fault analysis data is generated.
[0128] In one embodiment, the second processing module 103 is used to perform trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result, specifically for:
[0129] Acquire fault characteristic data related to the target leakage fault mode to obtain relevant fault characteristic data;
[0130] Performing fluctuation scale analysis according to the relevant fault characteristic data and the leakage fault characteristic data to obtain fluctuation scale data;
[0131] Determining a weighted shift parameter based on the fluctuation scale data;
[0132] Predicting the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set;
[0133] A result evaluation is performed based on the predicted leakage fault data set to obtain a fault trend analysis result.
[0134] In one embodiment, the second processing module 103 is used to predict the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set, specifically for:
[0135] The following formula is used to predict the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set:
[0136]
[0137] Among them, LD t represents the predicted leakage fault data at time point t in the predicted leakage fault data set; m represents the time window length; i represents the index from 1 to the time window length m in the weighted moving average calculation; w i Indicates the weight coefficient corresponding to time point t-i+1 when the time window length is m; y t-i+1 Represents the actual data value at time point t-i+1 in the time series.
[0138] In one embodiment, the third processing module 104 is used as the third processing module, and is used to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, specifically for:
[0139] Perform voltage conversion calculation and current conversion calculation according to the reference fault analysis data and the leakage detection data to obtain voltage basic conversion data and current basic conversion data;
[0140] Performing phase relationship analysis according to the voltage basic transformation data and the current basic transformation data to obtain phase relationship data;
[0141] Performing spectrum analysis according to the phase relationship data, the voltage basic transformation data, and the current basic transformation data to obtain harmonic content data;
[0142] The harmonic content data is combined with a circuit model to analyze the harmonic propagation path to obtain fault location data.
[0143] The present invention provides a low-voltage leakage remote sensing device based on Bluetooth communication. The device performs leakage detection on a target low-voltage household incoming line to obtain leakage detection data, and transmits the leakage detection data to the data analysis module via Bluetooth, so as to further perform data analysis on the received leakage detection data to obtain reference fault analysis data, so as to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, thereby generating a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data, and then obtaining fault acceptance data after the fault is repaired according to the fault repair plan, and performing acceptance analysis on the fault acceptance data to generate a fault acceptance result, which is beneficial to improving the accuracy of the low-voltage leakage detection process and can achieve the purpose of more efficient and accurate low-voltage leakage detection.
[0144] For the specific definition of the low-voltage leakage telemetry device based on Bluetooth communication, please refer to the definition of the low-voltage leakage telemetry system based on Bluetooth communication above, which will not be repeated here. Each module in the above-mentioned low-voltage leakage telemetry device based on Bluetooth communication can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0145] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it realizes a function or step on the service side of a low-voltage leakage telemetry system based on Bluetooth communication.
[0146] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it realizes a function or step on the client side of a low-voltage leakage telemetry system based on Bluetooth communication.
[0147] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:
[0148] Conduct leakage detection on the target low-voltage household incoming line to obtain leakage detection data;
[0149] Transmitting the leakage detection data to the data analysis module via Bluetooth;
[0150] Analyze the received leakage detection data to obtain reference fault analysis data;
[0151] Perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data;
[0152] Generate a fault repair plan based on the fault location data, the reference fault analysis data and the leakage detection data;
[0153] Fault acceptance data after the fault is repaired according to the fault repair plan is obtained, and acceptance analysis is performed on the fault acceptance data to generate a fault acceptance result.
[0154] The present invention provides a computer device, which performs leakage detection on a target low-voltage household incoming line to obtain leakage detection data, and transmits the leakage detection data to the data analysis module via Bluetooth, so as to further perform data analysis on the received leakage detection data to obtain reference fault analysis data, so as to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, so as to generate a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data, and then obtain fault acceptance data after the fault is repaired according to the fault repair plan, and perform acceptance analysis on the fault acceptance data to generate a fault acceptance result, which is conducive to improving the accuracy of the low-voltage leakage detection process and can achieve the purpose of more efficient and accurate low-voltage leakage detection.
[0155] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
[0156] Conduct leakage detection on the target low-voltage household incoming line to obtain leakage detection data;
[0157] Transmitting the leakage detection data to the data analysis module via Bluetooth;
[0158] Analyze the received leakage detection data to obtain reference fault analysis data;
[0159] Perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data;
[0160] Generate a fault repair plan based on the fault location data, the reference fault analysis data and the leakage detection data;
[0161] Fault acceptance data after the fault is repaired according to the fault repair plan is obtained, and acceptance analysis is performed on the fault acceptance data to generate a fault acceptance result.
[0162] The present invention provides a computer-readable storage medium, which performs leakage detection on a target low-voltage service-entry line to obtain leakage detection data, and transmits the leakage detection data to the data analysis module via Bluetooth, so as to further perform data analysis on the received leakage detection data to obtain reference fault analysis data, so as to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data, thereby generating a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data, and then obtaining fault acceptance data after the fault is repaired according to the fault repair plan, and performing acceptance analysis on the fault acceptance data to generate a fault acceptance result, which is beneficial to improving the accuracy of the low-voltage leakage detection process and can achieve the purpose of more efficient and accurate low-voltage leakage detection.
[0163] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.
[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0165] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0166] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.
Claims
1. A low voltage leakage remote sensing system based on Bluetooth communication, characterized in that: The low-voltage leakage telemetry system based on Bluetooth communication includes a leakage detection module, a Bluetooth transmission module, a data analysis module, a fault location module, a fault repair module and an acceptance module, wherein: The leakage detection module performs leakage detection on the target low-voltage household line to obtain leakage detection data; The Bluetooth transmission module transmits the leakage detection data to the data analysis module via Bluetooth; The data analysis module performs data analysis on the received leakage detection data to obtain reference fault analysis data; The fault location module performs fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data; The fault repair module generates a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data; The fault acceptance module obtains fault acceptance data after the fault is repaired according to the fault repair solution, and performs acceptance analysis on the fault acceptance data to generate a fault acceptance result.
2. The low-voltage leakage remote sensing system based on Bluetooth communication according to claim 1 is characterized in that: The data analysis module includes a data receiving submodule, a data preprocessing submodule, a fault feature extraction submodule, a fault pattern matching submodule, a fault trend analysis submodule and an analysis data generation submodule, wherein: The data receiving submodule receives the leakage detection data and arranges the leakage detection data in time sequence to obtain time sequence detection data; The data preprocessing submodule preprocesses the time series detection data to obtain preprocessed detection data; The fault feature extraction submodule extracts fault features from the pre-processed detection data to obtain leakage fault feature data; The fault mode matching submodule matches the leakage fault characteristic data with the leakage mode data in the leakage mode database to obtain a target leakage fault mode; The fault trend analysis submodule performs trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result; The analysis data generation submodule generates reference fault analysis data according to the fault trend analysis result.
3. The low-voltage leakage remote sensing system based on Bluetooth communication according to claim 2 is characterized in that: The fault trend analysis submodule includes a related data acquisition submodule, a fluctuation scale analysis submodule, a weighted parameter determination submodule, a leakage fault prediction submodule and a result evaluation submodule, wherein: The relevant data acquisition submodule acquires fault characteristic data related to the target leakage fault mode to obtain relevant fault characteristic data; The fluctuation scale analysis submodule performs fluctuation scale analysis according to the relevant fault characteristic data and the leakage fault characteristic data to obtain fluctuation scale data; The weighted parameter determination submodule determines the weighted movement parameter according to the fluctuation scale data; The leakage fault prediction submodule predicts the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set; The result evaluation submodule performs result evaluation based on the predicted leakage fault data set to obtain a fault trend analysis result.
4. The low-voltage leakage remote sensing system based on Bluetooth communication according to claim 3 is characterized in that: in, The leakage fault prediction submodule predicts the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data using the following formula to obtain a predicted leakage fault data set: Among them, LD t represents the predicted leakage fault data at time point t in the predicted leakage fault data set; m represents the time window length; i represents the index from 1 to the time window length m in the weighted moving average calculation; w i Indicates the weight coefficient corresponding to time point t-i+1 when the time window length is m; y t-i+1 Represents the actual data value at time point t-i+1 in the time series.
5. The low voltage leakage remote sensing system based on Bluetooth communication according to claim 4 is characterized in that: The fault location module includes an electrical transformation calculation submodule, a phase relationship analysis submodule, a spectrum analysis submodule and a propagation path analysis submodule, wherein: The electrical conversion calculation submodule performs voltage conversion calculation and current conversion calculation according to the reference fault analysis data and the leakage detection data to obtain voltage basic conversion data and current basic conversion data; The phase relationship analysis submodule performs phase relationship analysis according to the voltage basic transformation data and the current basic transformation data to obtain phase relationship data; The spectrum analysis submodule performs spectrum analysis according to the phase relationship data, the voltage basic transformation data and the current basic transformation data to obtain harmonic content data; The propagation path analysis submodule combines the harmonic content data with a circuit model to analyze the harmonic propagation path to obtain fault location data.
6. A low voltage leakage remote sensing device based on Bluetooth communication, characterized in that: The low-voltage leakage remote sensing device based on Bluetooth communication comprises: The first processing module is used to perform leakage detection on the target low-voltage household incoming line to obtain leakage detection data; A transmission module, used for transmitting the leakage detection data to the data analysis module via Bluetooth; A second processing module is used to analyze the received leakage detection data to obtain reference fault analysis data; A third processing module is used to perform fault location according to the reference fault analysis data and the leakage detection data to obtain fault location data; A fourth processing module, configured to generate a fault repair plan according to the fault location data, the reference fault analysis data and the leakage detection data; The fifth processing module is used to obtain fault acceptance data after the fault is repaired according to the fault repair plan, and perform acceptance analysis on the fault acceptance data to generate a fault acceptance result.
7. The low-voltage leakage remote sensing device based on Bluetooth communication according to claim 6, characterized in that: The second processing module is used to analyze the received leakage detection data to obtain reference fault analysis data, specifically for: Receiving the leakage detection data and arranging the leakage detection data in time sequence to obtain time sequence detection data; Preprocessing the time series detection data to obtain preprocessed detection data; Extracting fault features from the pre-processed detection data to obtain leakage fault feature data; Matching the leakage fault characteristic data with the leakage mode data in the leakage mode database to obtain a target leakage fault mode; Performing trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result; According to the fault trend analysis result, reference fault analysis data is generated.
8. The low-voltage leakage remote sensing device based on Bluetooth communication according to claim 7, characterized in that: The second processing module is used to perform trend analysis according to the target leakage fault mode and the leakage fault characteristic data to obtain a fault trend analysis result, specifically for: Acquire fault characteristic data related to the target leakage fault mode to obtain relevant fault characteristic data; Performing fluctuation scale analysis according to the relevant fault characteristic data and the leakage fault characteristic data to obtain fluctuation scale data; Determining a weighted shift parameter based on the fluctuation scale data; Predicting the leakage fault data in a preset time period according to the weighted movement parameter and the fault characteristic data to obtain a predicted leakage fault data set; A result evaluation is performed based on the predicted leakage fault data set to obtain a fault trend analysis result.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the processing method of the low-voltage leakage telemetry system based on Bluetooth communication as described in any one of claims 1 to 5 is implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processing method of the low-voltage leakage telemetry system based on Bluetooth communication as described in any one of claims 1 to 5 is implemented.